Identification of inland fresh water wetland using SAR and ETM+ data

The main aim of this paper was to explore the potential of SAR data, in combination with optical remote sensing data, in identifying inland fresh water wetland from crop, especially rice paddy. The test area is a part of Hongze Lake, the fourth biggest fresh water lake in China. It is one of important wetlands for migratory birds in China. Due to unreasonable exploitation of wetland resources, the lake is facing a great loss of wetland. In Hongze lake watershed, Jiangsu Provincial Sihong Hongze Lake wetland ecological reserve was established for the preserve of wetland ecosystem and rare species in the watershed. In the processing of the dataset, clustering algorithm ISODATA was employed firstly to generate initial classification results for sample selection. Then, 1500 samples were taken in total by using stratified random sampling. These samples were superimposed on the screen on top of rectified aerial images. The land cover class at each point was determined based on field investigation and visual interpretation. 900 samples of them were for training and the other for the assessment of classification accuracy. Attributes of samples such as the digital number values of six bands of ETM+( TM1-5, 7), texture, DEM and 4 components of principal components analysis of six bands of ETM + data, were fed into the CART (Classification and Regression Tree) algorithm for the generation of knowledge rules. Because the training observations were evenly distributed among classes, the class assignment at each terminal node was determined by the majority of per-class observations at that node. Then, decision tree classifier was applied to the imagery of ETM+ for the classification of landuse/cover in the whole study area. RADARSAT SAR C-band was classified into four classes: lowest backscatter, low backscatter, medium and high backscatter. The results from two data sources were combined by using rules. The results showed that the combination of the SAR data and the optical remot- ely sensed data have achieved the highest classification accuracy (92.3% of total classification accuracy). The results also confirmed the value of classification tree in the identification of fresh water wetland. It was illustrated that radar data was a good complementary data source for the identification of wetland.